Infrared and Visible Image Fusion Using Detail Enhanced Channel Attention Network

Cui, Yinghan; Du, Heng; Mei, Wenbo

doi:10.1109/access.2019.2959034

Cited by 12 publications

(9 citation statements)

References 34 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The two architectures in Figures 3(a Figure 3(a) shows an early fusion architecture, which performs feature extraction and feature fusion simultaneously, followed by image reconstruction in an end-to-end manner. Owing to its effectiveness in removing the correlation between two source images, many algorithms [25], [26], [29], [30], [38], [42], [44], [51], [52] using this architecture have recently been proposed. In particular, several researches [38], [44] have focused on designing network architectures for effective extraction of useful features from source images and their fusion.…”

Section: B Learning-based Fusionmentioning

confidence: 99%

Multiscale Progressive Fusion of Infrared and Visible Images

Park

Lee

2022

IEEE Access

View full text Add to dashboard Cite

Infrared and visible image fusion aims to generate more informative images of a given scene by combining multimodal images with complementary information. Although recent learning-based approaches have shown significant fusion performance, developing an effective fusion algorithm that can preserve complementary information while preventing bias toward either of the source images remains a significant challenge. In this work, we propose a multiscale progressive fusion (MPFusion) algorithm that extracts and progressively fuses multiscale features of infrared and visible images. The proposed algorithm consists of two networks, IRNet and FusionNet, which extract the intrinsic features of infrared and visible images, respectively. We transfer the multiscale information of the infrared image from IRNet to FusionNet to generate an informative fusion result. To this end, we develop the multi-dilated residual block (MDRB) and the progressive fusion block (PFB), which progressively combines the multiscale features from IRNet with those from FusionNet to fuse complementary features effectively and adaptively. Furthermore, we exploit edge-guided attention maps to preserve complementary edge information in the source images during fusion. Experimental results on several datasets demonstrate that the proposed algorithm outperforms state-of-the-art infrared and visible image fusion algorithms on both quantitative and qualitative comparisons.

show abstract

Section: B Learning-based Fusionmentioning

confidence: 99%

Multiscale Progressive Fusion of Infrared and Visible Images

Park

Lee

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…where IB is the visible image. According to Equations (20) and ( 21), the gradient map~B I  after Gamma transformation can be expressed as follows: 19) and ( 22), the fused image of detail layer F detail can be derived as follows:…”

Section: Fusion Of Detail Layermentioning

confidence: 99%

“…With the development of deep learning, many innovative image fusion methods based on deep learning are designed [19][20][21]. The convolutional neural network (CNN) attracts much focus due to its ability of powerful feature representation.…”

Section: Introductionmentioning

confidence: 99%

An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient Information

Han

Liu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Infrared and visible image fusion is a hot topic due to the perfect complementarity of their information. There are two key problems in infrared and visible image fusion. One is how to extract significant target areas and rich texture details from the source images, and the other is how to integrate them to produce satisfactory fused images. To tackle these problems, we propose a novel fusion framework in this paper. A multi-level image decomposition method is used to obtain the base layer and detail layer of the source image. For the fusion of base layer, an ingenious fusion strategy guided by the saliency map of source image is designed to improve the intensity of salient targets and the visual quality of the fused image. For the fusion of detail layer, an efficient approach by introducing the enhanced gradient information is presented to boost the detail features and sharpen the edges of the fused image. Experimental results demonstrate that, compared with fifteen classical and advanced fusion methods, the proposed image fusion framework has better performance in both subjective and objective evaluation.

show abstract

“…Figure provides an example of this point. Nowadays, the infrared and visible image fusion methods have been widely used in object detection, [ 1,2 ] object recognition, [ 3 ] semantic segmentation, [ 4 ] image enhancement, [ 5 ] remote sensing, [ 6 ] video surveillance, [ 7 ] medical imaging, [ 8 ] multimodal image fusion, [ 9–11 ] and industrial applications. [ 12 ]…”

Section: Introductionmentioning

confidence: 99%

EMA‐GAN: A Generative Adversarial Network for Infrared and Visible Image Fusion with Multiscale Attention Network and Expectation Maximization Algorithm

Xi,

Jin,

Jiang

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

The purpose of the infrared and visible image fusion is to generate a fused image with rich information. Although most fusion methods can achieve good performance, there are still shortcomings in extracting feature information from source images, which make it difficult to balance the thermal radiation region information and texture detail information in the fused image. To address the above issues, an expectation maximization (EM) learning framework based on adversarial generative networks (GAN) for infrared and visible image fusion is proposed. The EM algorithm (EMA) can obtain maximum likelihood estimation for problems with potential variables, which is helpful in solving the problem of lack of labels in infrared and visible image fusion. The axial‐corner attention mechanism is designed to capture long‐range semantic information and texture information of the visible image. The multifrequency attention mechanism digs the relationships between features at different scales to highlight target information of infrared images in the fused result. Meanwhile, two discriminators are used to balance two different features, and a new loss function is designed to maximize the likelihood estimate of the data with soft class label assignments, which is obtained from the expectation network. Extensive experiments demonstrate the superiority of EMA‐GAN over the state‐of‐the‐art.

show abstract

Infrared and Visible Image Fusion Using Detail Enhanced Channel Attention Network

Cited by 12 publications

References 34 publications

Multiscale Progressive Fusion of Infrared and Visible Images

Multiscale Progressive Fusion of Infrared and Visible Images

An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient Information

EMA‐GAN: A Generative Adversarial Network for Infrared and Visible Image Fusion with Multiscale Attention Network and Expectation Maximization Algorithm

Contact Info

Product

Resources

About